Why banks are betting big on artificial intelligence

By Hani Hagras, Chief Science Officer at Temenos
Hani Hagras, Chief Science Officer at Temenos discusses the role of artificial intelligence in banking and why the industry is adopting the technology

Before the pandemic, AI in banking was primarily used to automate routine tasks. But banks now see it as a vital tool to support product innovation, develop new business models, and provide a personalised experience for every customer.

A recent Economist Intelligence Unit (EIU) survey of banking executives for Temenos found that 85% have a “clear strategy” for adopting AI to develop new products and services.

It revealed over a third are prioritising AI to improve customer experience through personalisation. Some are also looking to acquire or partner with fintech companies to enhance their customer experience through a personalised experience when offering investments, saving deposits, and retail lending. 

Banks are investing massively in developing their AI capabilities. Last year JP Morgan Chase spent over US$12bn on technology and is betting big on AI and machine learning to get more value out of its data.

Emerging use cases for AI in banking

With the data-driven nature of the banking industry providing fertile soil for artificial intelligence, it is no surprise that AI is becoming a game-changer for banks. According to the EIU survey, four in five banking executives agree that unlocking value from AI will distinguish the winners from losers in the industry.

The EIU survey highlights that fraud detection is the top application of AI by banks. For example, Mastercard uses AI to predict and detect fraud precisely and quickly by analysing data on transactions and authorisations. The company has built a database of fraud patterns from which the AI system can learn. 

These improvements in data accuracy reduce financial losses and enhance the quality of spotting fraudulent transactions, reducing the hold-up of legitimate transactions to improve the customer experience.

With AI, banking providers can create highly tailored services that address anticipated customer needs. Chatbots and innovations such as “smile-to-pay” for frictionless transactions also rely on AI. Companies can also use machine learning techniques to analyse customer transactions, calculate individual default risks, and offer cheaper loans in real-time.

Balancing the risks and rewards

However, while AI provides a breadth of opportunity, banks must understand its limitations to help build a strong relationship with their customers. According to the EIU report, 62 percent of banks still believe that the complexity and risks of handling personal data for AI projects often outweigh the benefits to customer experience.

Most notably, trust and bias continue to be prominent barriers for customers. For example, Apple experienced an unfortunate AI-related incident in 2019. Its algorithms - used to decide whether to grant credit lines - ran into claims that it was exhibiting gender bias, allocating relatively fewer credit lines to women than men. This is just one example of the risks associated with AI-based decisions. And the necessity to be transparent and help educate to restore trust from consumers and regulators alike.

For “high-risk” applications, such as credit scoring, banks are likely to feel increased scrutiny from regulatory and customer influences. Specifically, the need for AI “explainability,” highlighting how an AI system comes to a decision. Banks will need to establish processes that allow users to understand the output created by machine learning algorithms. The success of AI will be improved by using explainable AI to spot and correct potential flaws and vulnerabilities in models.

Hyper-personalised experiences

Looking at this through the prism of Buy Now Pay Later, Explainable AI can inform customers why a particular flavor of BNPL is recommended and why alternatives are not as suitable. This hyper-personalised experience provides customers with the transparency and information to take control of their own banking decisions, increasing their trust and understanding of the service.

Strong explanatory capabilities will continue to be a core requirement for AI technology to gain customers’ trust while also ensuring banks meet the likely tightening regulatory requirements. EU regulators are seeking to establish stricter rules around artificial intelligence in areas like crime prediction, credit scoring, employee performance management and border control systems. In particular, the bloc seeks to mitigate undesirable outcomes and risks arising from AI-generated decisions.

AI must be explainable

While AI is clearly a game-changer, banks must ensure it is transparent to build acceptance and trust. And by ensuring any potential biases are mitigated, society can benefit from the opportunities that AI offers in the fast-changing banking environment. 


Featured Articles

What Dell and Super Micro can Bring Musk’s xAI Supercomputer

Elon Musk's xAI partnership with server hosting titans Dell and Super Micro could see his ambition for 'the world's largest supercomputer' lift off

Toshiba Takes Another Step to Ushering in Embodied AI

Toshiba's Cambridge Research Lab has announced two breakthroughs in Embodied AI alongside a new group to renew focus on the tech

Why AWS is Investing $230m in Credits for Gen AI Startups

Amazon is investing US$230m in AWS cloud credits to entice Gen AI startups to get onboard with using its cloud services

How Retrieval Augmented Generation (RAG) Enhances Gen AI

AI Applications

Synechron’s Prag Jaodekar on the UK's AI Regulation Journey

AI Strategy

LGBTQ+ in AI: Vivienne Ming and the Human Power of AI

Machine Learning